Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations4745
Missing cells2406
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory539.9 B

Variable types

Text3
Categorical7
Numeric10

Alerts

AST is highly overall correlated with PTSHigh correlation
DRAFT_NUMBER is highly overall correlated with DRAFT_ROUNDHigh correlation
DRAFT_ROUND is highly overall correlated with DRAFT_NUMBERHigh correlation
DRAFT_YEAR is highly overall correlated with FROM_YEAR and 3 other fieldsHigh correlation
FROM_YEAR is highly overall correlated with DRAFT_YEAR and 3 other fieldsHigh correlation
HEIGHT is highly overall correlated with WEIGHTHigh correlation
PTS is highly overall correlated with AST and 1 other fieldsHigh correlation
REB is highly overall correlated with PTSHigh correlation
ROSTER_STATUS is highly overall correlated with DRAFT_YEAR and 3 other fieldsHigh correlation
STATS_TIMEFRAME is highly overall correlated with DRAFT_YEAR and 3 other fieldsHigh correlation
TEAM_ABBREVIATION is highly overall correlated with TEAM_CITY and 1 other fieldsHigh correlation
TEAM_CITY is highly overall correlated with TEAM_ABBREVIATION and 1 other fieldsHigh correlation
TEAM_NAME is highly overall correlated with TEAM_ABBREVIATION and 1 other fieldsHigh correlation
TO_YEAR is highly overall correlated with DRAFT_YEAR and 3 other fieldsHigh correlation
WEIGHT is highly overall correlated with HEIGHTHigh correlation
DRAFT_YEAR has 1129 (23.8%) missing values Missing
DRAFT_ROUND has 1277 (26.9%) missing values Missing
JERSEY_NUMBER has 106 (2.2%) zeros Zeros
DRAFT_NUMBER has 1338 (28.2%) zeros Zeros
PTS has 107 (2.3%) zeros Zeros
REB has 190 (4.0%) zeros Zeros
AST has 239 (5.0%) zeros Zeros

Reproduction

Analysis started2025-07-31 08:12:51.749827
Analysis finished2025-07-31 08:13:09.217994
Duration17.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct4707
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size286.6 KiB
2025-07-31T16:13:09.596030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length22
Mean length12.595785
Min length4

Characters and Unicode

Total characters59767
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4671 ?
Unique (%)98.4%

Sample

1st rowAlaa Abdelnaby
2nd rowZaid Abdul-Aziz
3rd rowKareem Abdul-Jabbar
4th rowMahmoud Abdul-Rauf
5th rowTariq Abdul-Wahad
ValueCountFrequency (%)
williams 84
 
0.9%
john 81
 
0.8%
johnson 73
 
0.8%
bob 61
 
0.6%
smith 61
 
0.6%
jones 60
 
0.6%
mike 57
 
0.6%
jim 56
 
0.6%
chris 56
 
0.6%
jr 47
 
0.5%
Other values (4190) 8959
93.4%
2025-07-31T16:13:10.173741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5312
 
8.9%
4850
 
8.1%
a 4528
 
7.6%
n 4379
 
7.3%
r 4152
 
6.9%
o 3798
 
6.4%
i 3551
 
5.9%
l 3005
 
5.0%
s 2431
 
4.1%
t 1864
 
3.1%
Other values (61) 21897
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5312
 
8.9%
4850
 
8.1%
a 4528
 
7.6%
n 4379
 
7.3%
r 4152
 
6.9%
o 3798
 
6.4%
i 3551
 
5.9%
l 3005
 
5.0%
s 2431
 
4.1%
t 1864
 
3.1%
Other values (61) 21897
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5312
 
8.9%
4850
 
8.1%
a 4528
 
7.6%
n 4379
 
7.3%
r 4152
 
6.9%
o 3798
 
6.4%
i 3551
 
5.9%
l 3005
 
5.0%
s 2431
 
4.1%
t 1864
 
3.1%
Other values (61) 21897
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5312
 
8.9%
4850
 
8.1%
a 4528
 
7.6%
n 4379
 
7.3%
r 4152
 
6.9%
o 3798
 
6.4%
i 3551
 
5.9%
l 3005
 
5.0%
s 2431
 
4.1%
t 1864
 
3.1%
Other values (61) 21897
36.6%

TEAM_CITY
Categorical

High correlation 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size265.3 KiB
Golden State
 
251
New York
 
234
Atlanta
 
234
Detroit
 
232
Sacramento
 
230
Other values (25)
3564 

Length

Max length13
Median length11
Mean length8.2320337
Min length2

Characters and Unicode

Total characters39061
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPortland
2nd rowHouston
3rd rowLos Angeles
4th rowDenver
5th rowSacramento

Common Values

ValueCountFrequency (%)
Golden State 251
 
5.3%
New York 234
 
4.9%
Atlanta 234
 
4.9%
Detroit 232
 
4.9%
Sacramento 230
 
4.8%
Philadelphia 216
 
4.6%
Boston 214
 
4.5%
Los Angeles 206
 
4.3%
Washington 190
 
4.0%
Oklahoma City 167
 
3.5%
Other values (20) 2571
54.2%

Length

2025-07-31T16:13:10.330274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 309
 
5.3%
golden 251
 
4.3%
state 251
 
4.3%
york 234
 
4.0%
atlanta 234
 
4.0%
detroit 232
 
4.0%
sacramento 230
 
4.0%
philadelphia 216
 
3.7%
boston 214
 
3.7%
los 206
 
3.5%
Other values (25) 3426
59.0%

Most occurring characters

ValueCountFrequency (%)
a 3985
 
10.2%
o 3815
 
9.8%
n 3534
 
9.0%
e 3355
 
8.6%
t 3191
 
8.2%
l 2579
 
6.6%
i 2134
 
5.5%
r 1497
 
3.8%
h 1454
 
3.7%
s 1395
 
3.6%
Other values (29) 12122
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39061
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3985
 
10.2%
o 3815
 
9.8%
n 3534
 
9.0%
e 3355
 
8.6%
t 3191
 
8.2%
l 2579
 
6.6%
i 2134
 
5.5%
r 1497
 
3.8%
h 1454
 
3.7%
s 1395
 
3.6%
Other values (29) 12122
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39061
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3985
 
10.2%
o 3815
 
9.8%
n 3534
 
9.0%
e 3355
 
8.6%
t 3191
 
8.2%
l 2579
 
6.6%
i 2134
 
5.5%
r 1497
 
3.8%
h 1454
 
3.7%
s 1395
 
3.6%
Other values (29) 12122
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39061
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3985
 
10.2%
o 3815
 
9.8%
n 3534
 
9.0%
e 3355
 
8.6%
t 3191
 
8.2%
l 2579
 
6.6%
i 2134
 
5.5%
r 1497
 
3.8%
h 1454
 
3.7%
s 1395
 
3.6%
Other values (29) 12122
31.0%

TEAM_NAME
Categorical

High correlation 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size257.8 KiB
Warriors
 
251
Knicks
 
234
Hawks
 
234
Pistons
 
232
Kings
 
230
Other values (25)
3564 

Length

Max length13
Median length12
Mean length6.6084299
Min length4

Characters and Unicode

Total characters31357
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrail Blazers
2nd rowRockets
3rd rowLakers
4th rowNuggets
5th rowKings

Common Values

ValueCountFrequency (%)
Warriors 251
 
5.3%
Knicks 234
 
4.9%
Hawks 234
 
4.9%
Pistons 232
 
4.9%
Kings 230
 
4.8%
76ers 216
 
4.6%
Celtics 214
 
4.5%
Lakers 206
 
4.3%
Wizards 190
 
4.0%
Thunder 167
 
3.5%
Other values (20) 2571
54.2%

Length

2025-07-31T16:13:10.507275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
warriors 251
 
5.1%
knicks 234
 
4.8%
hawks 234
 
4.8%
pistons 232
 
4.7%
kings 230
 
4.7%
76ers 216
 
4.4%
celtics 214
 
4.4%
lakers 206
 
4.2%
wizards 190
 
3.9%
thunder 167
 
3.4%
Other values (21) 2725
55.6%

Most occurring characters

ValueCountFrequency (%)
s 4491
14.3%
r 2948
 
9.4%
e 2571
 
8.2%
i 2456
 
7.8%
a 2239
 
7.1%
l 1431
 
4.6%
n 1204
 
3.8%
c 1185
 
3.8%
t 1170
 
3.7%
k 1111
 
3.5%
Other values (28) 10551
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 4491
14.3%
r 2948
 
9.4%
e 2571
 
8.2%
i 2456
 
7.8%
a 2239
 
7.1%
l 1431
 
4.6%
n 1204
 
3.8%
c 1185
 
3.8%
t 1170
 
3.7%
k 1111
 
3.5%
Other values (28) 10551
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 4491
14.3%
r 2948
 
9.4%
e 2571
 
8.2%
i 2456
 
7.8%
a 2239
 
7.1%
l 1431
 
4.6%
n 1204
 
3.8%
c 1185
 
3.8%
t 1170
 
3.7%
k 1111
 
3.5%
Other values (28) 10551
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 4491
14.3%
r 2948
 
9.4%
e 2571
 
8.2%
i 2456
 
7.8%
a 2239
 
7.1%
l 1431
 
4.6%
n 1204
 
3.8%
c 1185
 
3.8%
t 1170
 
3.7%
k 1111
 
3.5%
Other values (28) 10551
33.6%

TEAM_ABBREVIATION
Categorical

High correlation 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size241.1 KiB
GSW
 
251
NYK
 
234
ATL
 
234
DET
 
232
SAC
 
230
Other values (25)
3564 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14235
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOR
2nd rowHOU
3rd rowLAL
4th rowDEN
5th rowSAC

Common Values

ValueCountFrequency (%)
GSW 251
 
5.3%
NYK 234
 
4.9%
ATL 234
 
4.9%
DET 232
 
4.9%
SAC 230
 
4.8%
PHI 216
 
4.6%
BOS 214
 
4.5%
LAL 206
 
4.3%
WAS 190
 
4.0%
OKC 167
 
3.5%
Other values (20) 2571
54.2%

Length

2025-07-31T16:13:10.663241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gsw 251
 
5.3%
nyk 234
 
4.9%
atl 234
 
4.9%
det 232
 
4.9%
sac 230
 
4.8%
phi 216
 
4.6%
bos 214
 
4.5%
lal 206
 
4.3%
was 190
 
4.0%
okc 167
 
3.5%
Other values (20) 2571
54.2%

Most occurring characters

ValueCountFrequency (%)
A 1608
 
11.3%
L 1341
 
9.4%
S 1135
 
8.0%
C 966
 
6.8%
O 946
 
6.6%
N 835
 
5.9%
I 834
 
5.9%
H 788
 
5.5%
T 687
 
4.8%
E 647
 
4.5%
Other values (11) 4448
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1608
 
11.3%
L 1341
 
9.4%
S 1135
 
8.0%
C 966
 
6.8%
O 946
 
6.6%
N 835
 
5.9%
I 834
 
5.9%
H 788
 
5.5%
T 687
 
4.8%
E 647
 
4.5%
Other values (11) 4448
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1608
 
11.3%
L 1341
 
9.4%
S 1135
 
8.0%
C 966
 
6.8%
O 946
 
6.6%
N 835
 
5.9%
I 834
 
5.9%
H 788
 
5.5%
T 687
 
4.8%
E 647
 
4.5%
Other values (11) 4448
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1608
 
11.3%
L 1341
 
9.4%
S 1135
 
8.0%
C 966
 
6.8%
O 946
 
6.6%
N 835
 
5.9%
I 834
 
5.9%
H 788
 
5.5%
T 687
 
4.8%
E 647
 
4.5%
Other values (11) 4448
31.2%

JERSEY_NUMBER
Real number (ℝ)

Zeros 

Distinct85
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.026554
Minimum0
Maximum99
Zeros106
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:10.821775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median17
Q331
95-th percentile51
Maximum99
Range99
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.883871
Coefficient of variation (CV)0.75541959
Kurtosis1.7797523
Mean21.026554
Median Absolute Deviation (MAD)10
Skewness1.1221715
Sum99771
Variance252.29736
MonotonicityNot monotonic
2025-07-31T16:13:11.005715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 296
 
6.2%
5 163
 
3.4%
11 158
 
3.3%
15 158
 
3.3%
20 158
 
3.3%
21 151
 
3.2%
10 151
 
3.2%
3 145
 
3.1%
4 143
 
3.0%
14 137
 
2.9%
Other values (75) 3085
65.0%
ValueCountFrequency (%)
0 106
2.2%
1 98
2.1%
2 107
2.3%
3 145
3.1%
4 143
3.0%
5 163
3.4%
6 103
2.2%
7 124
2.6%
8 125
2.6%
9 124
2.6%
ValueCountFrequency (%)
99 4
0.1%
98 3
0.1%
97 2
< 0.1%
96 1
 
< 0.1%
95 2
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
91 3
0.1%
90 1
 
< 0.1%

POSITION
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size232.9 KiB
G
1846 
F
1724 
C
645 
G-F
196 
F-C
 
140
Other values (2)
194 

Length

Max length3
Median length1
Mean length1.223393
Min length1

Characters and Unicode

Total characters5805
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowC
3rd rowC
4th rowG
5th rowF-G

Common Values

ValueCountFrequency (%)
G 1846
38.9%
F 1724
36.3%
C 645
 
13.6%
G-F 196
 
4.1%
F-C 140
 
3.0%
C-F 114
 
2.4%
F-G 80
 
1.7%

Length

2025-07-31T16:13:11.184654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T16:13:11.316195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
g 1846
38.9%
f 1724
36.3%
c 645
 
13.6%
g-f 196
 
4.1%
f-c 140
 
3.0%
c-f 114
 
2.4%
f-g 80
 
1.7%

Most occurring characters

ValueCountFrequency (%)
F 2254
38.8%
G 2122
36.6%
C 899
 
15.5%
- 530
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2254
38.8%
G 2122
36.6%
C 899
 
15.5%
- 530
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2254
38.8%
G 2122
36.6%
C 899
 
15.5%
- 530
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2254
38.8%
G 2122
36.6%
C 899
 
15.5%
- 530
 
9.1%

HEIGHT
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.272729
Minimum63
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:11.469301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile73
Q176
median78
Q381
95-th percentile84
Maximum91
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5346118
Coefficient of variation (CV)0.04515764
Kurtosis-0.20436898
Mean78.272729
Median Absolute Deviation (MAD)3
Skewness-0.099555945
Sum371404.1
Variance12.49348
MonotonicityNot monotonic
2025-07-31T16:13:11.616247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
79 490
10.3%
77 475
10.0%
80 469
9.9%
78 456
9.6%
81 443
9.3%
75 401
8.5%
82 372
7.8%
76 370
7.8%
74 294
6.2%
83 247
 
5.2%
Other values (19) 728
15.3%
ValueCountFrequency (%)
63 1
 
< 0.1%
65 1
 
< 0.1%
66 1
 
< 0.1%
67 6
 
0.1%
68 5
 
0.1%
69 10
 
0.2%
70 35
 
0.7%
71 53
 
1.1%
72 121
2.6%
73 197
4.2%
ValueCountFrequency (%)
91 1
 
< 0.1%
90 3
 
0.1%
89 5
 
0.1%
88 6
 
0.1%
87 13
 
0.3%
86 33
 
0.7%
85 55
 
1.2%
84 181
3.8%
83 247
5.2%
82 372
7.8%

WEIGHT
Real number (ℝ)

High correlation 

Distinct149
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.46872
Minimum133
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:11.787316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum133
5-th percentile175
Q1190
median210
Q3230
95-th percentile260
Maximum360
Range227
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.618965
Coefficient of variation (CV)0.12528416
Kurtosis0.21246895
Mean212.46872
Median Absolute Deviation (MAD)20
Skewness0.47063687
Sum1008164.1
Variance708.5693
MonotonicityNot monotonic
2025-07-31T16:13:11.964263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 311
 
6.6%
190 295
 
6.2%
220 280
 
5.9%
185 263
 
5.5%
215 259
 
5.5%
195 242
 
5.1%
200 241
 
5.1%
205 224
 
4.7%
225 224
 
4.7%
180 189
 
4.0%
Other values (139) 2217
46.7%
ValueCountFrequency (%)
133 2
< 0.1%
140 1
 
< 0.1%
141 1
 
< 0.1%
145 1
 
< 0.1%
150 3
0.1%
152 1
 
< 0.1%
153 1
 
< 0.1%
155 1
 
< 0.1%
156 1
 
< 0.1%
158 1
 
< 0.1%
ValueCountFrequency (%)
360 1
 
< 0.1%
330 1
 
< 0.1%
325 2
< 0.1%
315 1
 
< 0.1%
311 1
 
< 0.1%
310 1
 
< 0.1%
307 1
 
< 0.1%
306 1
 
< 0.1%
305 4
0.1%
303 1
 
< 0.1%
Distinct677
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size276.0 KiB
2025-07-31T16:13:12.280881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length10.525395
Min length1

Characters and Unicode

Total characters49943
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique322 ?
Unique (%)6.8%

Sample

1st rowDuke
2nd rowIowa State
3rd rowUCLA
4th rowLouisiana State
5th rowSan Jose State
ValueCountFrequency (%)
state 695
 
9.7%
carolina 163
 
2.3%
kentucky 146
 
2.0%
north 144
 
2.0%
michigan 131
 
1.8%
st 117
 
1.6%
kansas 103
 
1.4%
ucla 99
 
1.4%
arizona 96
 
1.3%
duke 95
 
1.3%
Other values (727) 5394
75.1%
2025-07-31T16:13:12.785559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5284
 
10.6%
e 4135
 
8.3%
n 3641
 
7.3%
t 3590
 
7.2%
i 3460
 
6.9%
o 3351
 
6.7%
r 2608
 
5.2%
2510
 
5.0%
s 2206
 
4.4%
l 1935
 
3.9%
Other values (54) 17223
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5284
 
10.6%
e 4135
 
8.3%
n 3641
 
7.3%
t 3590
 
7.2%
i 3460
 
6.9%
o 3351
 
6.7%
r 2608
 
5.2%
2510
 
5.0%
s 2206
 
4.4%
l 1935
 
3.9%
Other values (54) 17223
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5284
 
10.6%
e 4135
 
8.3%
n 3641
 
7.3%
t 3590
 
7.2%
i 3460
 
6.9%
o 3351
 
6.7%
r 2608
 
5.2%
2510
 
5.0%
s 2206
 
4.4%
l 1935
 
3.9%
Other values (54) 17223
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5284
 
10.6%
e 4135
 
8.3%
n 3641
 
7.3%
t 3590
 
7.2%
i 3460
 
6.9%
o 3351
 
6.7%
r 2608
 
5.2%
2510
 
5.0%
s 2206
 
4.4%
l 1935
 
3.9%
Other values (54) 17223
34.5%
Distinct80
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size243.4 KiB
2025-07-31T16:13:12.996243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length3
Mean length3.5036881
Min length3

Characters and Unicode

Total characters16625
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.5%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowFrance
ValueCountFrequency (%)
usa 4222
87.7%
canada 50
 
1.0%
france 49
 
1.0%
serbia 34
 
0.7%
australia 32
 
0.7%
croatia 21
 
0.4%
brazil 18
 
0.4%
spain 16
 
0.3%
argentina 16
 
0.3%
germany 15
 
0.3%
Other values (87) 340
 
7.1%
2025-07-31T16:13:13.372271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4321
26.0%
A 4273
25.7%
U 4242
25.5%
a 682
 
4.1%
i 355
 
2.1%
n 354
 
2.1%
e 348
 
2.1%
r 297
 
1.8%
t 153
 
0.9%
o 144
 
0.9%
Other values (38) 1456
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4321
26.0%
A 4273
25.7%
U 4242
25.5%
a 682
 
4.1%
i 355
 
2.1%
n 354
 
2.1%
e 348
 
2.1%
r 297
 
1.8%
t 153
 
0.9%
o 144
 
0.9%
Other values (38) 1456
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4321
26.0%
A 4273
25.7%
U 4242
25.5%
a 682
 
4.1%
i 355
 
2.1%
n 354
 
2.1%
e 348
 
2.1%
r 297
 
1.8%
t 153
 
0.9%
o 144
 
0.9%
Other values (38) 1456
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4321
26.0%
A 4273
25.7%
U 4242
25.5%
a 682
 
4.1%
i 355
 
2.1%
n 354
 
2.1%
e 348
 
2.1%
r 297
 
1.8%
t 153
 
0.9%
o 144
 
0.9%
Other values (38) 1456
 
8.8%

DRAFT_YEAR
Real number (ℝ)

High correlation  Missing 

Distinct78
Distinct (%)2.2%
Missing1129
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean1990.7069
Minimum1947
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:13.516812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1947
5-th percentile1954
Q11975
median1991
Q32009
95-th percentile2021
Maximum2024
Range77
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.676506
Coefficient of variation (CV)0.010386515
Kurtosis-0.9758973
Mean1990.7069
Median Absolute Deviation (MAD)17
Skewness-0.18423881
Sum7198396
Variance427.51792
MonotonicityNot monotonic
2025-07-31T16:13:13.704876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018 72
 
1.5%
1986 68
 
1.4%
2019 68
 
1.4%
2017 67
 
1.4%
1978 67
 
1.4%
1982 66
 
1.4%
1985 65
 
1.4%
2016 65
 
1.4%
1977 63
 
1.3%
1974 62
 
1.3%
Other values (68) 2953
62.2%
(Missing) 1129
 
23.8%
ValueCountFrequency (%)
1947 13
0.3%
1948 23
0.5%
1949 27
0.6%
1950 31
0.7%
1951 21
0.4%
1952 25
0.5%
1953 24
0.5%
1954 25
0.5%
1955 19
0.4%
1956 26
0.5%
ValueCountFrequency (%)
2024 55
1.2%
2023 55
1.2%
2022 52
1.1%
2021 56
1.2%
2020 58
1.2%
2019 68
1.4%
2018 72
1.5%
2017 67
1.4%
2016 65
1.4%
2015 48
1.0%

DRAFT_ROUND
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing1277
Missing (%)26.9%
Memory size246.1 KiB
2.0
1847 
1.0
1611 
0.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10404
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 1847
38.9%
1.0 1611
34.0%
0.0 10
 
0.2%
(Missing) 1277
26.9%

Length

2025-07-31T16:13:13.883372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T16:13:13.982902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1847
53.3%
1.0 1611
46.5%
0.0 10
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 3478
33.4%
. 3468
33.3%
2 1847
17.8%
1 1611
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3478
33.4%
. 3468
33.3%
2 1847
17.8%
1 1611
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3478
33.4%
. 3468
33.3%
2 1847
17.8%
1 1611
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3478
33.4%
. 3468
33.3%
2 1847
17.8%
1 1611
15.5%

DRAFT_NUMBER
Real number (ℝ)

High correlation  Zeros 

Distinct167
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.079031
Minimum0
Maximum221
Zeros1338
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:14.120435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q336
95-th percentile73
Maximum221
Range221
Interquartile range (IQR)36

Descriptive statistics

Standard deviation28.395044
Coefficient of variation (CV)1.2303396
Kurtosis7.8947979
Mean23.079031
Median Absolute Deviation (MAD)15
Skewness2.2855911
Sum109510
Variance806.27853
MonotonicityNot monotonic
2025-07-31T16:13:14.300058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1338
28.2%
5 78
 
1.6%
3 76
 
1.6%
4 76
 
1.6%
1 75
 
1.6%
6 75
 
1.6%
9 74
 
1.6%
2 73
 
1.5%
7 73
 
1.5%
10 71
 
1.5%
Other values (157) 2736
57.7%
ValueCountFrequency (%)
0 1338
28.2%
1 75
 
1.6%
2 73
 
1.5%
3 76
 
1.6%
4 76
 
1.6%
5 78
 
1.6%
6 75
 
1.6%
7 73
 
1.5%
8 69
 
1.5%
9 74
 
1.6%
ValueCountFrequency (%)
221 1
< 0.1%
215 1
< 0.1%
214 1
< 0.1%
211 1
< 0.1%
204 1
< 0.1%
202 1
< 0.1%
198 1
< 0.1%
190 1
< 0.1%
187 1
< 0.1%
185 2
< 0.1%

ROSTER_STATUS
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size241.1 KiB
0.0
4211 
1.0
534 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14235
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4211
88.7%
1.0 534
 
11.3%

Length

2025-07-31T16:13:14.466136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T16:13:14.555675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4211
88.7%
1.0 534
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 8956
62.9%
. 4745
33.3%
1 534
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8956
62.9%
. 4745
33.3%
1 534
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8956
62.9%
. 4745
33.3%
1 534
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8956
62.9%
. 4745
33.3%
1 534
 
3.8%

PTS
Real number (ℝ)

High correlation  Zeros 

Distinct257
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3593256
Minimum0
Maximum32.7
Zeros107
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:14.685213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.8
median5.1
Q38.6
95-th percentile16.7
Maximum32.7
Range32.7
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.9291547
Coefficient of variation (CV)0.77510652
Kurtosis2.2683171
Mean6.3593256
Median Absolute Deviation (MAD)2.7
Skewness1.4132376
Sum30175
Variance24.296566
MonotonicityNot monotonic
2025-07-31T16:13:14.874278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107
 
2.3%
2 92
 
1.9%
1 66
 
1.4%
3 66
 
1.4%
3.5 65
 
1.4%
2.8 65
 
1.4%
3.2 64
 
1.3%
2.3 63
 
1.3%
1.8 62
 
1.3%
3.3 61
 
1.3%
Other values (247) 4034
85.0%
ValueCountFrequency (%)
0 107
2.3%
0.1 1
 
< 0.1%
0.2 3
 
0.1%
0.3 9
 
0.2%
0.4 6
 
0.1%
0.5 11
 
0.2%
0.6 15
 
0.3%
0.7 21
 
0.4%
0.8 27
 
0.6%
0.9 23
 
0.5%
ValueCountFrequency (%)
32.7 1
< 0.1%
30.4 1
< 0.1%
30.1 2
< 0.1%
29.6 1
< 0.1%
28.2 1
< 0.1%
27.6 1
< 0.1%
27.4 1
< 0.1%
27 1
< 0.1%
26.8 1
< 0.1%
26.7 1
< 0.1%

REB
Real number (ℝ)

High correlation  Zeros 

Distinct138
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8648261
Minimum0
Maximum22.9
Zeros190
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:15.067352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.3
median2.3
Q33.9
95-th percentile7.4
Maximum22.9
Range22.9
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation2.2662194
Coefficient of variation (CV)0.79104954
Kurtosis5.34901
Mean2.8648261
Median Absolute Deviation (MAD)1.2
Skewness1.7373417
Sum13593.6
Variance5.1357503
MonotonicityNot monotonic
2025-07-31T16:13:15.268384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 190
 
4.0%
1 180
 
3.8%
1.5 142
 
3.0%
1.3 134
 
2.8%
1.4 123
 
2.6%
1.1 122
 
2.6%
1.8 120
 
2.5%
1.2 118
 
2.5%
2.4 113
 
2.4%
2 112
 
2.4%
Other values (128) 3391
71.5%
ValueCountFrequency (%)
0 190
4.0%
0.1 5
 
0.1%
0.2 18
 
0.4%
0.3 47
 
1.0%
0.4 38
 
0.8%
0.5 82
1.7%
0.6 76
 
1.6%
0.7 83
1.7%
0.8 98
2.1%
0.9 94
2.0%
ValueCountFrequency (%)
22.9 1
< 0.1%
22.5 1
< 0.1%
17.3 1
< 0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15 1
< 0.1%
14 1
< 0.1%
13.9 1
< 0.1%
13.7 1
< 0.1%
13.6 1
< 0.1%

AST
Real number (ℝ)

High correlation  Zeros 

Distinct94
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4529399
Minimum0
Maximum11.6
Zeros239
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:15.460035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q32
95-th percentile4.38
Maximum11.6
Range11.6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4173002
Coefficient of variation (CV)0.97547059
Kurtosis5.5360842
Mean1.4529399
Median Absolute Deviation (MAD)0.6
Skewness2.0105248
Sum6894.2
Variance2.0087398
MonotonicityNot monotonic
2025-07-31T16:13:15.655767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 284
 
6.0%
0.3 272
 
5.7%
0.6 244
 
5.1%
0.7 242
 
5.1%
0 239
 
5.0%
0.8 238
 
5.0%
0.4 232
 
4.9%
1 204
 
4.3%
0.2 199
 
4.2%
0.9 192
 
4.0%
Other values (84) 2399
50.6%
ValueCountFrequency (%)
0 239
5.0%
0.1 115
2.4%
0.2 199
4.2%
0.3 272
5.7%
0.4 232
4.9%
0.5 284
6.0%
0.6 244
5.1%
0.7 242
5.1%
0.8 238
5.0%
0.9 192
4.0%
ValueCountFrequency (%)
11.6 1
< 0.1%
11.2 1
< 0.1%
10.5 1
< 0.1%
10.2 1
< 0.1%
9.5 1
< 0.1%
9.3 1
< 0.1%
9.2 1
< 0.1%
9.1 2
< 0.1%
8.9 1
< 0.1%
8.7 2
< 0.1%

STATS_TIMEFRAME
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size255.0 KiB
Career
4176 
Season
569 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters28470
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCareer
2nd rowCareer
3rd rowCareer
4th rowCareer
5th rowCareer

Common Values

ValueCountFrequency (%)
Career 4176
88.0%
Season 569
 
12.0%

Length

2025-07-31T16:13:15.814296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-31T16:13:15.898835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
career 4176
88.0%
season 569
 
12.0%

Most occurring characters

ValueCountFrequency (%)
e 8921
31.3%
r 8352
29.3%
a 4745
16.7%
C 4176
14.7%
S 569
 
2.0%
s 569
 
2.0%
o 569
 
2.0%
n 569
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8921
31.3%
r 8352
29.3%
a 4745
16.7%
C 4176
14.7%
S 569
 
2.0%
s 569
 
2.0%
o 569
 
2.0%
n 569
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8921
31.3%
r 8352
29.3%
a 4745
16.7%
C 4176
14.7%
S 569
 
2.0%
s 569
 
2.0%
o 569
 
2.0%
n 569
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8921
31.3%
r 8352
29.3%
a 4745
16.7%
C 4176
14.7%
S 569
 
2.0%
s 569
 
2.0%
o 569
 
2.0%
n 569
 
2.0%

FROM_YEAR
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1993.1989
Minimum1946
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:16.022371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1946
5-th percentile1952
Q11977
median1995
Q32012
95-th percentile2022
Maximum2024
Range78
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.618717
Coefficient of variation (CV)0.010846241
Kurtosis-0.83296231
Mean1993.1989
Median Absolute Deviation (MAD)18
Skewness-0.41919155
Sum9457729
Variance467.36893
MonotonicityNot monotonic
2025-07-31T16:13:16.204441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021 127
 
2.7%
2017 121
 
2.6%
2019 117
 
2.5%
1976 109
 
2.3%
2018 108
 
2.3%
2024 106
 
2.2%
2023 100
 
2.1%
2020 95
 
2.0%
2022 89
 
1.9%
2016 88
 
1.9%
Other values (69) 3685
77.7%
ValueCountFrequency (%)
1946 51
1.1%
1947 18
 
0.4%
1948 51
1.1%
1949 56
1.2%
1950 22
 
0.5%
1951 25
0.5%
1952 26
0.5%
1953 25
0.5%
1954 33
0.7%
1955 29
0.6%
ValueCountFrequency (%)
2024 106
2.2%
2023 100
2.1%
2022 89
1.9%
2021 127
2.7%
2020 95
2.0%
2019 117
2.5%
2018 108
2.3%
2017 121
2.6%
2016 88
1.9%
2015 72
1.5%

TO_YEAR
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1997.7395
Minimum1946
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2025-07-31T16:13:16.385492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1946
5-th percentile1954.2
Q11981
median2002
Q32018
95-th percentile2024
Maximum2024
Range78
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.915848
Coefficient of variation (CV)0.010970323
Kurtosis-0.72845115
Mean1997.7395
Median Absolute Deviation (MAD)17
Skewness-0.56859195
Sum9479274
Variance480.30439
MonotonicityNot monotonic
2025-07-31T16:13:17.011806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 575
 
12.1%
2021 154
 
3.2%
2023 126
 
2.7%
2017 116
 
2.4%
2018 112
 
2.4%
2002 100
 
2.1%
2004 87
 
1.8%
2016 83
 
1.7%
2011 83
 
1.7%
2019 78
 
1.6%
Other values (69) 3231
68.1%
ValueCountFrequency (%)
1946 18
0.4%
1947 12
 
0.3%
1948 27
0.6%
1949 32
0.7%
1950 29
0.6%
1951 24
0.5%
1952 36
0.8%
1953 26
0.5%
1954 34
0.7%
1955 18
0.4%
ValueCountFrequency (%)
2024 575
12.1%
2023 126
 
2.7%
2022 78
 
1.6%
2021 154
 
3.2%
2020 67
 
1.4%
2019 78
 
1.6%
2018 112
 
2.4%
2017 116
 
2.4%
2016 83
 
1.7%
2015 75
 
1.6%

Interactions

2025-07-31T16:13:06.723124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:53.341937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:54.778084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.193224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:57.862623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.368799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.736955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:02.176491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.802053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.255944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:06.869935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:53.478970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:54.921622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.330773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.011543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.510867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.878164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:02.604158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.939598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.399486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:07.019080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:53.624630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.061229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.463856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.160078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.644059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.036609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:02.734132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.080663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.543049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:07.160112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:53.759183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.197346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.600173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.317710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.772344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.175777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:02.871888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.217194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.681216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:07.313642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:53.903903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.341829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.759708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.466253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.920550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.327960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.008284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.374259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.830787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:07.457718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:54.036344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.475216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.883446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.607083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.045698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.475961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.137637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.512799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.971861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-31T16:12:54.178292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.615623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:57.315636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.757623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.179991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.612833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.271687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.673594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:06.118233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:07.752153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:54.317830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.752161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:57.444871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:58.910298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.306275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.742275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.393267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.802744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:06.260524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:07.906385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:54.466384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:55.897245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:57.580786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.058753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.435351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:01.881581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.522964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:04.938284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:06.404929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:08.058212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:54.616006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:56.050336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:57.718004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:12:59.211627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:00.586427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:02.028792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:03.664512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:05.099288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-31T16:13:06.561579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-31T16:13:17.182058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ASTDRAFT_NUMBERDRAFT_ROUNDDRAFT_YEARFROM_YEARHEIGHTJERSEY_NUMBERPOSITIONPTSREBROSTER_STATUSSTATS_TIMEFRAMETEAM_ABBREVIATIONTEAM_CITYTEAM_NAMETO_YEARWEIGHT
AST1.000-0.0180.181-0.028-0.105-0.404-0.2190.1640.7580.4240.1620.1520.0150.0150.0150.021-0.355
DRAFT_NUMBER-0.0181.0000.5270.097-0.0840.0670.0850.000-0.035-0.0050.0790.0840.0430.0430.043-0.0820.018
DRAFT_ROUND0.1810.5271.0000.1670.1490.0860.0570.1160.3030.2460.1670.1680.0330.0330.0330.1930.089
DRAFT_YEAR-0.0280.0970.1671.0000.9970.168-0.1420.122-0.001-0.0270.6710.6810.1770.1770.1770.9730.259
FROM_YEAR-0.105-0.0840.1490.9971.0000.136-0.1070.112-0.103-0.0660.5840.6090.1850.1850.1850.9710.218
HEIGHT-0.4040.0670.0860.1680.1361.0000.3350.424-0.0360.4110.0550.0560.0310.0310.0310.1490.823
JERSEY_NUMBER-0.2190.0850.057-0.142-0.1070.3351.0000.152-0.0850.1000.1300.1350.0530.0530.053-0.1130.299
POSITION0.1640.0000.1160.1220.1120.4240.1521.0000.0470.1830.0940.0920.0310.0310.0310.1210.347
PTS0.758-0.0350.303-0.001-0.103-0.036-0.0850.0471.0000.7280.2270.2110.0180.0180.0180.050-0.018
REB0.424-0.0050.246-0.027-0.0660.4110.1000.1830.7281.0000.1330.1200.0000.0000.0000.0670.392
ROSTER_STATUS0.1620.0790.1670.6710.5840.0550.1300.0940.2270.1331.0000.9510.0940.0940.0940.5760.075
STATS_TIMEFRAME0.1520.0840.1680.6810.6090.0560.1350.0920.2110.1200.9511.0000.1160.1160.1160.5970.077
TEAM_ABBREVIATION0.0150.0430.0330.1770.1850.0310.0530.0310.0180.0000.0940.1161.0001.0001.0000.1780.029
TEAM_CITY0.0150.0430.0330.1770.1850.0310.0530.0310.0180.0000.0940.1161.0001.0001.0000.1780.029
TEAM_NAME0.0150.0430.0330.1770.1850.0310.0530.0310.0180.0000.0940.1161.0001.0001.0000.1780.029
TO_YEAR0.021-0.0820.1930.9730.9710.149-0.1130.1210.0500.0670.5760.5970.1780.1780.1781.0000.242
WEIGHT-0.3550.0180.0890.2590.2180.8230.2990.347-0.0180.3920.0750.0770.0290.0290.0290.2421.000

Missing values

2025-07-31T16:13:08.650762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-31T16:13:08.903540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-31T16:13:09.124446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PLAYER_NAMETEAM_CITYTEAM_NAMETEAM_ABBREVIATIONJERSEY_NUMBERPOSITIONHEIGHTWEIGHTCOLLEGECOUNTRYDRAFT_YEARDRAFT_ROUNDDRAFT_NUMBERROSTER_STATUSPTSREBASTSTATS_TIMEFRAMEFROM_YEARTO_YEAR
0Alaa AbdelnabyPortlandTrail BlazersPOR30F82.0240.0DukeUSA1990.01.025.00.05.73.30.3Career19901994
1Zaid Abdul-AzizHoustonRocketsHOU54C81.0235.0Iowa StateUSA1968.01.05.00.09.08.01.2Career19681977
2Kareem Abdul-JabbarLos AngelesLakersLAL33C86.0225.0UCLAUSA1969.01.01.00.024.611.23.6Career19691988
3Mahmoud Abdul-RaufDenverNuggetsDEN1G73.0162.0Louisiana StateUSA1990.01.03.00.014.61.93.5Career19902000
4Tariq Abdul-WahadSacramentoKingsSAC9F-G78.0235.0San Jose StateFrance1997.01.011.00.07.83.31.1Career19972003
5Shareef Abdur-RahimMemphisGrizzliesMEM3F81.0245.0CaliforniaUSA1996.01.03.00.018.17.52.5Career19962007
6Tom AbernethyGolden StateWarriorsGSW5F79.0220.0IndianaUSA1976.02.043.00.05.63.21.2Career19761980
7Forest AblePhiladelphia76ersPHI6G75.0180.0Western KentuckyUSA1956.0NaN0.00.00.01.01.0Career19561956
8Alex AbrinesOklahoma CityThunderOKC8G78.0190.0FC BarcelonaSpain2013.02.032.00.05.31.40.5Career20162018
9Precious AchiuwaNew YorkKnicksNYK5F80.0243.0MemphisNigeria2020.01.020.01.06.65.61.0Season20202024
PLAYER_NAMETEAM_CITYTEAM_NAMETEAM_ABBREVIATIONJERSEY_NUMBERPOSITIONHEIGHTWEIGHTCOLLEGECOUNTRYDRAFT_YEARDRAFT_ROUNDDRAFT_NUMBERROSTER_STATUSPTSREBASTSTATS_TIMEFRAMEFROM_YEARTO_YEAR
4735Derrick ZimmermanBrooklynNetsBKN12G75.0195.0Mississippi StateUSA2003.02.040.00.02.02.03.5Career20052005
4736Stephen ZimmermanLos AngelesLakersLAL33C84.0240.0UNLVUSA2016.02.041.00.01.21.80.2Career20162016
4737Paul ZipserChicagoBullsCHI16F80.0226.0Bayern MunichGermany2016.02.048.00.04.72.60.8Career20162017
4738Ante ZizicClevelandCavaliersCLE41C82.0266.0DarussafakaCroatia2016.01.023.00.06.03.90.6Career20172019
4739Jim ZoetDetroitPistonsDET34C85.0240.0Kent StateUSANaNNaN0.00.00.31.10.1Career19821982
4740Bill ZopfMilwaukeeBucksMIL6G73.0170.0DuquesneUSA1970.02.033.00.02.20.91.4Career19701970
4741Ivica ZubacLAClippersLAC40C84.0240.0Mega BasketCroatia2016.02.032.01.016.812.62.7Season20162024
4742Tristan da SilvaOrlandoMagicORL23F80.0217.0ColoradoGermany2024.01.018.01.07.23.31.5Season20242024
4743Vlatko ČančarDenverNuggetsDEN31F80.0236.0San Pablo BurgosSlovenia2017.02.049.01.01.82.50.7Season20192024
4744Dario ŠarićDenverNuggetsDEN9F-C82.0225.0Anadolu EfesCroatia2014.01.012.01.03.53.11.4Season20162024